Deep learning has increased the performance of the medical image diagnostic frameworks [1]}, [2]} by many folds, especially to predict abnormal lung pathologies from the CXRs [3]}. The majority of these systems are based on transfer learning or fine-tuning approaches, which utilize pre-trained models such as VGG-19 [4]}, MobileNet [5]} to classify abnormal chest pathologies [6]}, [7]}, [8]} using CXRs. Although transfer learning systems can fulfill the large-scale data requirement (to some extent) for recognizing a limited set of chest pathologies [9]}, [10]}, [11]}. However, these systems tend to forget their source domain knowledge (while they are tuned for the target domain tasks) [12]}. Due to this, they cannot be deployed in hospitals for screening purposes. In clinical practice, the classification model is expected to recognize new types of pathologies (while retaining its previously learned knowledge) using very few training examples [13]}, [14]}. Incremental learning enables the deep learning models to meet this requirement. More specifically, the concept of incremental learning originates to periodically learn scarce yet inter-related tasks effectively without re-training the model from scratch [15]}. Furthermore, by showing high resistance to catastrophic forgetting, the deep incremental learning models can eliminate the need for re-training on the large datasets to learn the limited number of classification categories [16]}, [17]}.
